Un criterio para seleccionar operadores genéticos para resolver CSP

Nuestro interés es definir algoritmos evolucionistas para la resolución de problemas de satisfacción de restricciones (CSP), los cuales tomen en cuenta tanto las ventajas de los métodos tradicionales de resolución de CSP, así como las características propias de este tipo de problemas. En este contexto, se propone en el presente artículo un criterio para poder evaluar la performance de los operadores gen'eticos dentro de algoritmos evolucionistas que resuelven CSP.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Un criterio para seleccionar operadores genéticos para resolver CSP

Nuestro interés es definir algoritmos evolucionistas para la resolución de problemas de satisfacción de restricciones (CSP), los cuales tomen en cuenta tanto las ventajas de los métodos tradicionales de resolución de CSP, así como las características propias de este tipo de problemas. En este contexto, se propone en el presente artículo un criterio para poder evaluar la performance de los operadores gen'eticos dentro de algoritmos evolucionistas que resuelven CSP.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Un criterio para seleccionar operadores genéticos para resolver CSP

Nuestro interés es definir algoritmos evolucionistas para la resolución de problemas de satisfacción de restricciones (CSP), los cuales tomen en cuenta tanto las ventajas de los métodos tradicionales de resolución de CSP, así como las características propias de este tipo de problemas. En este contexto, se propone en el presente artículo un criterio para poder evaluar la performance de los operadores gen'eticos dentro de algoritmos evolucionistas que resuelven CSP.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A criteria to select genetic operators for solving CSP

Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.Facultad de Informátic

A criteria to select genetic operators for solving CSP

Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.Facultad de Informátic

A criteria to select genetic operators for solving CSP

Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.Facultad de Informátic

From quasi-solutions to solution: An Evolutionary Algorithm to solve CSP

This paper describes an Evolutionary Algorithm that repairs to solve Constraint Satisfaction Problems. Knowledge about prop erties of the constraints network can permit to define a fitness function which is used to improve the stochastic search. A selection mechanism which exploits this fitness function has been deøned. The algorithm has been tested by running experiments on randomly generated 3-colouring graphs, with dioeerent constraints networks. We have also designed a specialized operator "permutation", which permits to improve the performance of the classic crossover operator, reducing the generations number and a faster convergence to a global optimum, when the population is staying in a local optimum. The results suggest that the technique may be successfully applied to other CSP

Improving harmony search algorithms by using tonal variation: the case of Sudoku and MKP

In this paper we propose an improved Harmony Search (HS) version inspired in the tonal variation of jazz musical improvisation. To evaluate our approach we considered two well-known problems, a Constraint Satisfaction Problem: Sudoku, and a Constraint Satisfaction Optimisation Problem: the Multidimensional Knapsack Problem (MKP). For each problem, we considered an existing baseline HS algorithm to implement our technique: the HS for Sudoku puzzles and, the Adaptive Binary HS for the MKP. The experiments showed that including tonal variation allows HS algorithms to find better quality solutions in both problems

Evolutionary Search guided by the Constraint Network to solve CSP

We are interested in defining a general evolutionary algorithm to solve Constraint Satisfaction Problems, which take into account both advantages of the systematic and traditional methods and of characteristics of the CSP. In this context knowledge about properties of the constraint network has allowed us to define a fitness function, for Evaluation, [15]. We introduce here two new operators which look at the constraint network during the evolution. The first one is a bisexual operator like crossover denominated arc-crossover, for Exploitation. The second one is an operator like mutation called arc-mutation, for Exploration. These operators are used to improve the stochastic search

Learning and focusing strategies to improve ACO that solves CSP

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